<ul><li>The structure of a simple decision tree can be explained as follows</li><li>In a CART structure, the variable at the top of the tree is the most important independent variable</li><li>Decision trees can also be visualized on a two-dimensional axis</li><li>For regression problems, the CART algorithm optimizes splits to minimize the Cost Function</li><li>Hyperparameters in CART (Classification and Regression Trees) significantly impact the model’s performance</li><li>Max_depth, min_samples_split, min_samples_leaf, max_features, criterion, and random_state are some of the hyperparameters of CART</li><li>Hyperparameter optimization can be performed using tools like GridSearchCV</li><li>CART (Classification and Regression Trees) systematically splits datasets into subgroups</li><li>This process is valuable in data analysis and modeling as it provides clarity and predictability</li><li>The success of CART lies in its cost function, which minimizes prediction errors by determining the correct split points</li></ul>

CART (Classification and Regression Trees)

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